Paper Info
Title
Inverse Optimal Control From Incomplete Trajectory Observations
Abstract
This article develops a methodology that enables learning an objective function of an optimal control system from incomplete trajectory observations. The objective function is assumed to be a weighted sum of features (or basis functions) with unknown weights, and the observed data is a segment of a trajectory of system states and inputs. The proposed technique introduces the concept of the recovery matrix to establish the relationship between any available segment of the trajectory and the weights of given candidate features. The rank of the recovery matrix indicates whether a subset of relevant features can be found among the candidate features and the corresponding weights can be learned from the segment data. The recovery matrix can be obtained iteratively and its rank non-decreasing property shows that additional observations may contribute to the objective learning. Based on the recovery matrix, a method for using incomplete trajectory observations to learn the weights of selected features is established, and an incremental inverse optimal control algorithm is developed by automatically finding the minimal required observation. The effectiveness of the proposed method is demonstrated on a linear quadratic regulator system and a simulated robot manipulator.
Year
DOI
Venue
2021
10.1177/0278364921996384
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Keywords
DocType
Volume
Inverse optimal control, inverse reinforcement learning, incomplete trajectory observations, objective learning
Journal
40
Issue
ISSN
Citations 
6-7
0278-3649
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wanxin Jin152.83
Dana Kulic281069.21
Shaoshuai Mou339529.80
Sandra Hirche4961106.36